In networked database technologies, hierarchical databases such as online analytical processing (OLAP) databases, extensible markup language (XML)-based data stores, and others are known. During operations with hierarchical data stores, it is sometimes necessary or desirable to add or insert additional data values into nodes which lie beneath the parent node in the hierarchy. For instance, if a parent node stores financial data reflecting annual profit for a corporation or other entity, it may be desired at the same time or a later time to insert lower-level breakdowns of that data over smaller intervals. For example, it may be desired to insert child nodes which store quarterly or monthly profit for that entity. In general, the process of pushing or distributing data down to child nodes or other lower-level or other destinations in the database structure can be referred to as “spreading” the data.
In known database platforms, the ability to perform spreading operations can be constrained or limited by limitations in the data structures and logical operations permitted on those platforms. For one, while platforms may exist which permit a user to insert a new child node at a lower level in relation to a parent node, data may only be distributed down from one parent at a time. If, for instance, annual profit is intended to be expanded or spread down to quarterly profit entries over the last ten years or some other group of years, the user may be forced to manually insert the child nodes and manually perform the spreading, year by year.
For another, when performing distribution of data between nodes or levels in a hierarchical data store, the order in which spreading is performed can have effects which the database engine does not take into account. For instance, when spreading annual profit down to quarterly nodes, it may be necessary to check for special charge offs or other factors against profit in a given quarter, or profit for a first fiscal quarter may be affected by a carry-over entry from the previous fiscal year end. Other factors or dependencies can apply, but existing database platforms do not permit the incorporation of dependency rules or other logic to ensure data accuracy or integrity.
As still another limitation, available database platforms generally construct the operative data stores in a flat or two-dimensional tree structure, with a root node descending via linked paths to lower-level nodes or leaves. Two-dimensional tree structures do not afford a native extension to three-dimensional data structures, in which each node maybe located in a three-dimensional space and link to other nodes in three dimensions, creating a richer data structure and/or computational pathways. Other shortcomings in existing database engines exist. It may be desirable to systems and methods for the conditioned distribution of data in a lattice-based database using spreading rules which permit the storage of data in a lattice configuration and the corresponding manipulation of data spreading operations in three dimensions, using sets of placeholder nodes and/or applying dependency rules or other conditioning logic to the data spreading operations.